Overview

Dataset statistics

Number of variables11
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory43.1 KiB
Average record size in memory88.3 B

Variable types

NUM11

Reproduction

Analysis started2020-08-25 00:56:08.575311
Analysis finished2020-08-25 00:56:27.252590
Duration18.68 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

oz1 has unique values Unique
oz2 has unique values Unique
oz3 has unique values Unique
oz4 has unique values Unique
oz5 has unique values Unique
oz6 has unique values Unique
oz7 has unique values Unique
oz8 has unique values Unique
oz9 has unique values Unique
oz10 has unique values Unique
target has unique values Unique

Variables

oz1
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.720679372549057e-10
Minimum-1.7942380905151367
Maximum1.7119427919387815
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:56:27.301162image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.794238091
5-th percentile-1.595194572
Q1-0.8522934169
median0.008222589968
Q30.870204702
95-th percentile1.513329697
Maximum1.711942792
Range3.506180882
Interquartile range (IQR)1.722498119

Descriptive statistics

Standard deviation0.9999999998
Coefficient of variation (CV)1028734681
Kurtosis-1.182950053
Mean9.720679373e-10
Median Absolute Deviation (MAD)0.8624354799
Skewness-0.09521533929
Sum4.860339686e-07
Variance0.9999999995
2020-08-25T00:56:27.411867image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.584960877910.2%
 
0.055720891810.2%
 
-0.686859548110.2%
 
0.879873514210.2%
 
-1.0651353610.2%
 
-0.388355165710.2%
 
-1.08662354910.2%
 
-0.124676831110.2%
 
0.0664498060910.2%
 
0.510116457910.2%
 
0.514999687710.2%
 
0.294397860810.2%
 
0.693714737910.2%
 
-1.32102549110.2%
 
1.56126022310.2%
 
1.01243960910.2%
 
0.197117745910.2%
 
-1.12767505610.2%
 
-0.112594120210.2%
 
-1.78588175810.2%
 
1.56958556210.2%
 
-0.63415503510.2%
 
-0.000252426019910.2%
 
0.539431095110.2%
 
-0.0358209870810.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.79423809110.2%
 
-1.78588175810.2%
 
-1.7646360410.2%
 
-1.75941753410.2%
 
-1.75802516910.2%
 
-1.74888491610.2%
 
-1.74791204910.2%
 
-1.74140608310.2%
 
-1.72825133810.2%
 
-1.71891820410.2%
 
ValueCountFrequency (%) 
1.71194279210.2%
 
1.70255839810.2%
 
1.69586074410.2%
 
1.66955399510.2%
 
1.65926361110.2%
 
1.64611184610.2%
 
1.6436885610.2%
 
1.64166414710.2%
 
1.63131868810.2%
 
1.60952734910.2%
 

oz2
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.276385203003883e-09
Minimum-1.7084802389144895
Maximum1.6922727823257446
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:56:27.539836image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.708480239
5-th percentile-1.564283574
Q1-0.8919336349
median0.0644945018
Q30.8492384404
95-th percentile1.55278095
Maximum1.692272782
Range3.400753021
Interquartile range (IQR)1.741172075

Descriptive statistics

Standard deviation0.9999999983
Coefficient of variation (CV)439292961.9
Kurtosis-1.178490874
Mean2.276385203e-09
Median Absolute Deviation (MAD)0.8625579588
Skewness-0.0322946032
Sum1.138192602e-06
Variance0.9999999966
2020-08-25T00:56:27.647419image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.862304389510.2%
 
-1.34996187710.2%
 
0.11860323710.2%
 
0.595711827310.2%
 
1.62405538610.2%
 
1.66471564810.2%
 
1.02018904710.2%
 
-1.50651884110.2%
 
1.69074428110.2%
 
0.254560530210.2%
 
-0.667424023210.2%
 
0.326339036210.2%
 
1.23309230810.2%
 
0.966458439810.2%
 
1.59767746910.2%
 
1.34624743510.2%
 
0.294249147210.2%
 
-1.00459492210.2%
 
-0.824564337710.2%
 
1.16671335710.2%
 
0.596056103710.2%
 
1.04367685310.2%
 
-0.975943744210.2%
 
-1.23365974410.2%
 
0.851922571710.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.70848023910.2%
 
-1.70335495510.2%
 
-1.68417882910.2%
 
-1.68022835310.2%
 
-1.67346394110.2%
 
-1.67326211910.2%
 
-1.65260565310.2%
 
-1.65103125610.2%
 
-1.63484585310.2%
 
-1.63250100610.2%
 
ValueCountFrequency (%) 
1.69227278210.2%
 
1.69074428110.2%
 
1.68045604210.2%
 
1.6752611410.2%
 
1.6749806410.2%
 
1.67412602910.2%
 
1.66662168510.2%
 
1.66570031610.2%
 
1.66471564810.2%
 
1.65413951910.2%
 

oz3
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-8.346978566109441e-10
Minimum-1.7264958620071411
Maximum1.6342203617095947
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:56:27.765711image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.726495862
5-th percentile-1.55207175
Q1-0.8687658161
median0.0351440683
Q30.9091661125
95-th percentile1.482582515
Maximum1.634220362
Range3.360716224
Interquartile range (IQR)1.777931929

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)-1198038299
Kurtosis-1.275425937
Mean-8.346978566e-10
Median Absolute Deviation (MAD)0.8888391554
Skewness-0.03168639402
Sum-4.173489283e-07
Variance1
2020-08-25T00:56:27.872318image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.944336593210.2%
 
1.20772886310.2%
 
-0.369829088410.2%
 
0.950849175510.2%
 
1.60075688410.2%
 
0.367997586710.2%
 
-1.07952010610.2%
 
-0.76802694810.2%
 
1.59051966710.2%
 
-1.13943064210.2%
 
-1.69599854910.2%
 
0.516946494610.2%
 
0.62046295410.2%
 
0.493337452410.2%
 
0.162684664110.2%
 
-1.22051560910.2%
 
-0.870504915710.2%
 
0.825546145410.2%
 
0.025310080510.2%
 
0.281224697810.2%
 
-0.0296742878910.2%
 
-0.283383578110.2%
 
-0.746456980710.2%
 
1.28393530810.2%
 
-1.59685671310.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.72649586210.2%
 
-1.72012305310.2%
 
-1.71670949510.2%
 
-1.70248746910.2%
 
-1.69907474510.2%
 
-1.69599854910.2%
 
-1.69565069710.2%
 
-1.68946564210.2%
 
-1.67234015510.2%
 
-1.66891562910.2%
 
ValueCountFrequency (%) 
1.63422036210.2%
 
1.62838125210.2%
 
1.62054789110.2%
 
1.62037289110.2%
 
1.61751413310.2%
 
1.61701905710.2%
 
1.61615848510.2%
 
1.61584007710.2%
 
1.60824930710.2%
 
1.60075688410.2%
 

oz4
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4854747354984286e-10
Minimum-1.735120415687561
Maximum1.8080251216888428
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:56:27.994987image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.735120416
5-th percentile-1.512728333
Q1-0.8718017191
median-0.004016700375
Q30.8401409686
95-th percentile1.596001112
Maximum1.808025122
Range3.543145537
Interquartile range (IQR)1.711942688

Descriptive statistics

Standard deviation0.9999999989
Coefficient of variation (CV)2869049627
Kurtosis-1.152014107
Mean3.485474735e-10
Median Absolute Deviation (MAD)0.8625841766
Skewness0.05510297168
Sum1.742737368e-07
Variance0.9999999979
2020-08-25T00:56:28.097502image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.286620557310.2%
 
1.2116370210.2%
 
0.0304360203410.2%
 
-0.307777762410.2%
 
0.292037069810.2%
 
-0.446728646810.2%
 
-1.05207490910.2%
 
-1.64128494310.2%
 
0.556979000610.2%
 
1.00848901310.2%
 
-0.805815637110.2%
 
-1.59365105610.2%
 
-0.387378007210.2%
 
-1.72335195510.2%
 
-0.916055500510.2%
 
-0.655917286910.2%
 
1.48708486610.2%
 
0.867540776710.2%
 
-1.14719557810.2%
 
1.7429006110.2%
 
1.02042305510.2%
 
-1.49906194210.2%
 
0.861687183410.2%
 
-0.87536168110.2%
 
-1.58666825310.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.73512041610.2%
 
-1.73040950310.2%
 
-1.72335195510.2%
 
-1.69833755510.2%
 
-1.69669282410.2%
 
-1.69052481710.2%
 
-1.68617522710.2%
 
-1.68521463910.2%
 
-1.66118812610.2%
 
-1.64128494310.2%
 
ValueCountFrequency (%) 
1.80802512210.2%
 
1.79891157210.2%
 
1.79635953910.2%
 
1.7917264710.2%
 
1.79124724910.2%
 
1.79039025310.2%
 
1.75943100510.2%
 
1.75276398710.2%
 
1.74967658510.2%
 
1.74906146510.2%
 

oz5
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.294778823852539e-09
Minimum-1.766493558883667
Maximum1.7081854343414309
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:56:28.212452image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.766493559
5-th percentile-1.597430766
Q1-0.9077546448
median0.05133193731
Q30.8356185555
95-th percentile1.52817027
Maximum1.708185434
Range3.474678993
Interquartile range (IQR)1.7433732

Descriptive statistics

Standard deviation1.000000002
Coefficient of variation (CV)435771845.1
Kurtosis-1.182497219
Mean2.294778824e-09
Median Absolute Deviation (MAD)0.8502734601
Skewness-0.06727863613
Sum1.147389412e-06
Variance1.000000004
2020-08-25T00:56:28.320559image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.944330275110.2%
 
1.12374687210.2%
 
-0.351719409210.2%
 
-1.18227040810.2%
 
-0.203204423210.2%
 
0.303871601810.2%
 
1.13175988210.2%
 
-0.858734846110.2%
 
-0.675141453710.2%
 
1.34247815610.2%
 
-1.05927717710.2%
 
1.69014000910.2%
 
-1.16865980610.2%
 
1.70705425710.2%
 
0.492468297510.2%
 
1.40695214310.2%
 
-0.777575671710.2%
 
-1.48995530610.2%
 
0.0914004743110.2%
 
1.43235421210.2%
 
-0.976924061810.2%
 
-0.140227094310.2%
 
-1.04564905210.2%
 
-0.534215807910.2%
 
-0.063742883510.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.76649355910.2%
 
-1.74864149110.2%
 
-1.74793791810.2%
 
-1.74393475110.2%
 
-1.7257679710.2%
 
-1.72150610.2%
 
-1.71937549110.2%
 
-1.71721756510.2%
 
-1.71245491510.2%
 
-1.69191646610.2%
 
ValueCountFrequency (%) 
1.70818543410.2%
 
1.70705425710.2%
 
1.69450211510.2%
 
1.69051945210.2%
 
1.69014000910.2%
 
1.67906677710.2%
 
1.67322909810.2%
 
1.65595054610.2%
 
1.655375610.2%
 
1.61379623410.2%
 

oz6
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.930166035890579e-09
Minimum-1.7054780721664429
Maximum1.6571121215820312
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:56:28.442358image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.705478072
5-th percentile-1.572873801
Q1-0.8903202415
median0.01585976768
Q30.896879673
95-th percentile1.494720453
Maximum1.657112122
Range3.362590194
Interquartile range (IQR)1.787199914

Descriptive statistics

Standard deviation1.000000001
Coefficient of variation (CV)-518090144.6
Kurtosis-1.254610968
Mean-1.930166036e-09
Median Absolute Deviation (MAD)0.8971336484
Skewness-0.06353290688
Sum-9.650830179e-07
Variance1.000000001
2020-08-25T00:56:28.556914image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.52733993510.2%
 
-1.33679842910.2%
 
0.850901484510.2%
 
0.608715593810.2%
 
-0.143878519510.2%
 
0.0725503861910.2%
 
0.0681560933610.2%
 
0.471843659910.2%
 
-1.67253434710.2%
 
-1.02995836710.2%
 
0.694100797210.2%
 
-1.58269584210.2%
 
0.603848993810.2%
 
-1.03191959910.2%
 
1.23504829410.2%
 
0.0602432414910.2%
 
-1.70192158210.2%
 
-0.057661820210.2%
 
-0.963235020610.2%
 
0.610698710.2%
 
-0.866559743910.2%
 
-0.0912301987410.2%
 
-0.234043255410.2%
 
0.138130113510.2%
 
0.493188530210.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.70547807210.2%
 
-1.70452654410.2%
 
-1.70192158210.2%
 
-1.69693839610.2%
 
-1.69429314110.2%
 
-1.68777465810.2%
 
-1.68172907810.2%
 
-1.68124222810.2%
 
-1.67330706110.2%
 
-1.67253434710.2%
 
ValueCountFrequency (%) 
1.65711212210.2%
 
1.64769923710.2%
 
1.63804042310.2%
 
1.63385212410.2%
 
1.63259804210.2%
 
1.61909854410.2%
 
1.61606442910.2%
 
1.61036193410.2%
 
1.60828089710.2%
 
1.60782051110.2%
 

oz7
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.816139936447144e-10
Minimum-1.755658745765686
Maximum1.8397253751754759
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:56:28.673615image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.755658746
5-th percentile-1.600147939
Q1-0.8497299403
median0.02014139807
Q30.8032152951
95-th percentile1.638136035
Maximum1.839725375
Range3.595384121
Interquartile range (IQR)1.652945235

Descriptive statistics

Standard deviation0.9999999993
Coefficient of variation (CV)1018730383
Kurtosis-1.086075216
Mean9.816139936e-10
Median Absolute Deviation (MAD)0.8491932801
Skewness0.01956382031
Sum4.908069968e-07
Variance0.9999999987
2020-08-25T00:56:28.777644image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.419432371910.2%
 
-1.62682020710.2%
 
-0.621403992210.2%
 
0.822578966610.2%
 
-1.10415005710.2%
 
0.0349320769310.2%
 
0.515944719310.2%
 
1.19985973810.2%
 
-0.289711713810.2%
 
-0.897574663210.2%
 
1.02213752310.2%
 
-0.614058256110.2%
 
-0.254887908710.2%
 
-0.809539794910.2%
 
1.56317043310.2%
 
-1.43622696410.2%
 
0.0779266655410.2%
 
-1.32880747310.2%
 
-0.349647879610.2%
 
-1.65694022210.2%
 
1.11202132710.2%
 
1.78975677510.2%
 
1.23343479610.2%
 
1.7546087510.2%
 
-0.391250103710.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.75565874610.2%
 
-1.73830544910.2%
 
-1.73527216910.2%
 
-1.7345725310.2%
 
-1.71670663410.2%
 
-1.70790684210.2%
 
-1.70708394110.2%
 
-1.70413780210.2%
 
-1.67009663610.2%
 
-1.66453707210.2%
 
ValueCountFrequency (%) 
1.83972537510.2%
 
1.83035063710.2%
 
1.82084763110.2%
 
1.82054817710.2%
 
1.81868255110.2%
 
1.81516563910.2%
 
1.80027365710.2%
 
1.79845142410.2%
 
1.78975677510.2%
 
1.78112328110.2%
 

oz8
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.6320846007999989e-09
Minimum-1.6766587495803833
Maximum1.7608959674835205
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:56:28.923831image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.67665875
5-th percentile-1.536410236
Q1-0.8670167625
median-0.01187154744
Q30.8557839692
95-th percentile1.576949739
Maximum1.760895967
Range3.437554717
Interquartile range (IQR)1.722800732

Descriptive statistics

Standard deviation1.000000002
Coefficient of variation (CV)-612713336.8
Kurtosis-1.191405465
Mean-1.632084601e-09
Median Absolute Deviation (MAD)0.8632479897
Skewness0.04468479979
Sum-8.160423004e-07
Variance1.000000003
2020-08-25T00:56:29.041443image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.510742068310.2%
 
0.582352459410.2%
 
-1.47520554110.2%
 
-0.136549845310.2%
 
-0.12931904210.2%
 
1.09118914610.2%
 
0.258942842510.2%
 
-0.266541838610.2%
 
-1.56578493110.2%
 
-1.53577816510.2%
 
1.20570325910.2%
 
0.645247340210.2%
 
0.816720306910.2%
 
1.22133302710.2%
 
1.01040029510.2%
 
0.817642450310.2%
 
1.06905758410.2%
 
0.0275198388810.2%
 
-0.519860386810.2%
 
0.0878381952610.2%
 
-1.49738156810.2%
 
1.34832322610.2%
 
-0.942145407210.2%
 
0.707368731510.2%
 
-0.920261800310.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.6766587510.2%
 
-1.67528152510.2%
 
-1.67165899310.2%
 
-1.6586184510.2%
 
-1.65229308610.2%
 
-1.64271581210.2%
 
-1.63648760310.2%
 
-1.62940943210.2%
 
-1.6292725810.2%
 
-1.62699174910.2%
 
ValueCountFrequency (%) 
1.76089596710.2%
 
1.73127114810.2%
 
1.72132885510.2%
 
1.71483898210.2%
 
1.70734012110.2%
 
1.7028882510.2%
 
1.69892215710.2%
 
1.68770754310.2%
 
1.68591129810.2%
 
1.67999863610.2%
 

oz9
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.056805744198868e-10
Minimum-1.7520724534988403
Maximum1.6898140907287598
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:56:29.162102image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.752072453
5-th percentile-1.645698601
Q1-0.8504866064
median0.05837640539
Q30.8540751189
95-th percentile1.513777542
Maximum1.689814091
Range3.441886544
Interquartile range (IQR)1.704561725

Descriptive statistics

Standard deviation1.000000004
Coefficient of variation (CV)-1417071746
Kurtosis-1.126355211
Mean-7.056805744e-10
Median Absolute Deviation (MAD)0.8479420543
Skewness-0.09782792186
Sum-3.528402872e-07
Variance1.000000008
2020-08-25T00:56:29.273417image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.35958874210.2%
 
-1.65298128110.2%
 
1.13927161710.2%
 
1.04054725210.2%
 
-0.635467529310.2%
 
-0.408355653310.2%
 
0.453766226810.2%
 
1.4615540510.2%
 
-1.45960235610.2%
 
0.962224066310.2%
 
-0.0374535024210.2%
 
1.39986860810.2%
 
0.0886623039810.2%
 
-0.798167288310.2%
 
0.853221058810.2%
 
-0.0423174202410.2%
 
0.735282897910.2%
 
0.138752862810.2%
 
1.68815565110.2%
 
1.20378470410.2%
 
0.335614442810.2%
 
-0.0934252217410.2%
 
-0.605801284310.2%
 
0.771817803410.2%
 
1.46551835510.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.75207245310.2%
 
-1.75005960510.2%
 
-1.74979734410.2%
 
-1.74955236910.2%
 
-1.74779605910.2%
 
-1.74164748210.2%
 
-1.73565602310.2%
 
-1.73480415310.2%
 
-1.73390746110.2%
 
-1.72510051710.2%
 
ValueCountFrequency (%) 
1.68981409110.2%
 
1.68815565110.2%
 
1.67979645710.2%
 
1.6556756510.2%
 
1.65295779710.2%
 
1.64688503710.2%
 
1.64402496810.2%
 
1.64211285110.2%
 
1.64015924910.2%
 
1.63398468510.2%
 

oz10
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7206184566020965e-09
Minimum-1.7247148752212524
Maximum1.70335590839386
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:56:29.413025image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.724714875
5-th percentile-1.573445815
Q1-0.8887870461
median0.02410405781
Q30.838746205
95-th percentile1.547579557
Maximum1.703355908
Range3.428070784
Interquartile range (IQR)1.727533251

Descriptive statistics

Standard deviation0.999999999
Coefficient of variation (CV)581186372.4
Kurtosis-1.175679528
Mean1.720618457e-09
Median Absolute Deviation (MAD)0.878748267
Skewness-0.04172032488
Sum8.603092283e-07
Variance0.999999998
2020-08-25T00:56:29.519036image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.732420921310.2%
 
0.242816239610.2%
 
1.30796372910.2%
 
-0.384608089910.2%
 
-1.46938383610.2%
 
-0.152179226310.2%
 
0.785893678710.2%
 
0.648758888210.2%
 
0.783529281610.2%
 
0.802086353310.2%
 
0.362958699510.2%
 
-1.47527813910.2%
 
-0.312084764210.2%
 
-0.114300452210.2%
 
-0.121503010410.2%
 
0.611669063610.2%
 
-0.425097912510.2%
 
1.64354264710.2%
 
-1.05735003910.2%
 
1.24094665110.2%
 
-0.860281705910.2%
 
0.965208411210.2%
 
-0.329285621610.2%
 
1.16675531910.2%
 
1.49097716810.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.72471487510.2%
 
-1.72409224510.2%
 
-1.72266507110.2%
 
-1.72002530110.2%
 
-1.71494257510.2%
 
-1.71227955810.2%
 
-1.69926643410.2%
 
-1.69620299310.2%
 
-1.68897056610.2%
 
-1.68010246810.2%
 
ValueCountFrequency (%) 
1.70335590810.2%
 
1.6813217410.2%
 
1.6797628410.2%
 
1.67287826510.2%
 
1.66260683510.2%
 
1.6617678410.2%
 
1.65609431310.2%
 
1.65287208610.2%
 
1.65257680410.2%
 
1.65178024810.2%
 

target
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.634486347436905e-10
Minimum-2.5215320587158203
Maximum2.83838438987732
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:56:29.643577image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.521532059
5-th percentile-1.608073062
Q1-0.7449035496
median0.03883939609
Q30.6997879893
95-th percentile1.622214228
Maximum2.83838439
Range5.359916449
Interquartile range (IQR)1.444691539

Descriptive statistics

Standard deviation1.000000001
Coefficient of variation (CV)-2751420436
Kurtosis-0.5771761972
Mean-3.634486347e-10
Median Absolute Deviation (MAD)0.7247280143
Skewness0.03268393985
Sum-1.817243174e-07
Variance1.000000002
2020-08-25T00:56:29.742979image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.658283889310.2%
 
-1.45962667510.2%
 
-0.279447555510.2%
 
0.586240351210.2%
 
1.20192968810.2%
 
-1.74670505510.2%
 
0.110679477510.2%
 
-1.02405309710.2%
 
1.24175775110.2%
 
-0.331698983910.2%
 
0.638008892510.2%
 
1.23891115210.2%
 
-0.242998704310.2%
 
-0.371253162610.2%
 
0.527664005810.2%
 
0.644138991810.2%
 
-0.655622661110.2%
 
1.53580546410.2%
 
0.0363974422210.2%
 
-0.245687738110.2%
 
1.12175202410.2%
 
-0.618622422210.2%
 
0.0197345204710.2%
 
-1.48587906410.2%
 
-0.0114196771810.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-2.52153205910.2%
 
-2.32580423410.2%
 
-2.15906810810.2%
 
-2.1382534510.2%
 
-2.13688516610.2%
 
-2.02622318310.2%
 
-1.93751215910.2%
 
-1.8414508110.2%
 
-1.80887424910.2%
 
-1.78736507910.2%
 
ValueCountFrequency (%) 
2.8383843910.2%
 
2.39730858810.2%
 
2.34956312210.2%
 
2.30999898910.2%
 
2.25858831410.2%
 
2.12549495710.2%
 
2.10021638910.2%
 
1.99215626710.2%
 
1.9584559210.2%
 
1.95145463910.2%
 

Interactions

2020-08-25T00:56:09.019249image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:09.154127image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:09.288451image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:09.421338image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:09.715741image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:09.848016image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:09.981326image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:10.117190image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:10.262557image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:10.396978image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:10.532452image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:10.661765image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:10.800672image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:10.936744image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:11.073316image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:11.209224image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:11.345333image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:11.480771image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:11.620397image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:11.764212image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:11.902890image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:12.036027image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:12.162629image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:12.299078image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:12.432432image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:12.564520image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:12.696801image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:12.832773image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:12.966923image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:13.104414image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:13.241990image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:13.375898image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:13.513046image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:13.812397image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:13.949568image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:14.093086image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:14.230148image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:14.363927image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:14.496760image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:14.629990image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:14.769806image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:14.903848image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:15.035276image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:15.172083image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:15.307159image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:15.440219image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:15.575400image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:15.708835image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:15.847417image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:15.979817image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:16.114661image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:16.252964image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:16.387242image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:16.521338image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:16.651275image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:16.789490image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:16.923540image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:17.057977image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:17.202399image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:17.351475image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:17.493453image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:17.632051image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:17.953504image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:18.088079image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:18.229263image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:18.371819image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:18.506280image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:18.653311image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:18.795129image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:18.937971image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:19.076845image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:19.213793image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:19.361858image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:19.511831image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:19.652704image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:19.804400image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:19.945501image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:20.086085image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:20.230707image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:20.382764image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:20.521625image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:20.666333image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:20.815152image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:20.969355image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:21.129068image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:21.276192image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:21.424562image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:21.562121image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:21.695963image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:21.835069image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:21.983329image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:22.295285image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:22.447907image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:22.604939image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:22.754928image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:22.901837image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:23.049737image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:23.190821image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:23.329457image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:23.465912image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:23.612367image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:23.766536image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:23.913841image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:24.049096image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:24.195356image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:24.330997image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:24.476317image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:24.612542image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:24.747456image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:24.885189image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:25.025470image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:25.172565image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:25.306623image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:25.455935image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:25.599310image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:25.739034image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:25.885971image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:26.028475image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:26.166314image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:26.300592image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:26.435296image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T00:56:29.866310image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-25T00:56:30.263631image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-25T00:56:30.488697image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-25T00:56:30.724000image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-25T00:56:26.868972image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:27.149873image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

oz1oz2oz3oz4oz5oz6oz7oz8oz9oz10target
01.3931451.5756810.329795-0.6678411.022937-0.378206-1.536834-0.113111-0.299131-0.624878-0.556299
1-0.632670-1.176123-0.763457-0.7692910.0408641.174980-0.1939100.4358420.462737-1.349083-1.342997
21.497247-0.6152240.455416-1.4257920.562369-0.8397350.7267161.476152-1.741647-1.249025-0.690794
3-1.2573791.615379-0.4004531.4870851.0762701.287806-0.9803701.3491350.0886621.2883841.121752
41.2258390.3497730.328042-0.907369-0.539883-1.152713-0.8477470.7523761.3184781.4597720.258180
50.895955-1.6732620.0535631.212145-1.485816-1.4807991.433120-0.7533130.4798081.451078-1.496805
60.357320-1.684179-0.772469-1.464947-1.5256991.284374-0.7426520.464016-1.5610881.005063-2.521532
7-1.176286-0.192442-1.6340591.7527640.840345-0.777682-1.3856240.3589181.575784-1.2875441.560423
80.477061-0.8004530.555730-1.508248-1.482743-1.2509831.715333-0.6364401.421538-0.317373-1.787365
9-1.3570001.469731-1.320469-0.8753621.4253560.2234940.9862881.636861-0.7981670.837041-0.406629

Last rows

oz1oz2oz3oz4oz5oz6oz7oz8oz9oz10target
4900.2191360.2335550.150251-0.2970631.460290-0.417779-0.897575-0.9101281.089948-0.1374470.595515
491-0.560476-1.6734641.3527751.1388881.4372701.394228-1.3110651.362542-1.651629-0.940562-0.024355
4920.8697511.4453950.194517-1.1975141.6559510.8587720.2153800.5867101.4113780.372137-0.028703
4931.695861-1.3499621.151194-1.0422890.829863-0.891262-1.607051-0.9319410.0446781.552847-0.912648
4941.2050701.3549680.384149-0.244817-1.237301-1.289336-0.117465-1.3802420.2279910.752375-0.556291
495-0.032862-1.632501-0.9765380.1316290.874526-1.4385930.1899190.4039631.642113-0.310663-0.853061
4961.1291530.741700-0.288167-1.7351200.0618531.4578030.1764171.3483231.528229-0.805500-0.761108
4971.4139171.0386800.3233610.4785991.280463-0.725406-0.897463-1.5874130.215136-0.9714590.910655
498-1.065135-0.2811251.6158401.698677-1.6234670.603849-0.827519-0.3550240.1706391.4302490.449468
499-1.764636-0.7956740.710123-0.7390720.4163910.518080-0.111118-1.0052720.2029770.394288-1.314858